class_docstr: A QSA class, inherited from Optimizer.
  
  This is the designed class to define QSA-related
  variables and methods.
  
  References:
      J. Zhang et al. Queuing search algorithm: A novel metaheuristic algorithm
      for solving engineering optimization problems.
      Applied Mathematical Modelling (2018).
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docstr: Wraps Queue Search Algorithm over all agents and variables.
  
  Args:
      space: Space containing agents and update-related information.
      function: A Function object that will be used as the objective function.
      iteration: Current iteration.
      n_iterations: Maximum number of iterations.
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code: def update(
      self,
      space: Space,
      function: Function,
      iteration: int,
      n_iterations: int,
  ) -> None:
      """Wraps Queue Search Algorithm over all agents and variables.
  
      Args:
          space: Space containing agents and update-related information.
          function: A Function object that will be used as the objective function.
          iteration: Current iteration.
          n_iterations: Maximum number of iterations.
  
      """
  
      beta = np.exp(
          np.log(1 / (iteration + c.EPSILON))
          * np.sqrt(iteration / n_iterations)
      )
  
      self._business_one(space.agents, function, beta)
      self._business_two(space.agents, function)
      self._business_three(space.agents, function)
  
file_path: opytimizer/opytimizer/optimizers/social/qsa.py
imports: import copy
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.constant
  from opytimizer.core import Optimizer
  from opytimizer.core.agent import Agent
  from opytimizer.core.function import Function
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 6 for calling the QSA obj's underlying member method code with example usage: obj.update(space, function, iteration, n_iterations) without return types